Update app.py

app.py

@@ -2,45 +2,49 @@ import gradio as gr
 import json
 from transformers import pipeline
 
-# Load AI models
+# Load Hugging Face models
 code_analyzer = pipeline("text-classification", model="microsoft/codebert-base")
 nlp_model = pipeline("text2text-generation", model="google/flan-t5-large")
 
-# Load FAQ fallback from JSON file
+# Load FAQ fallback (if available)
 try:
     with open("faq.json", "r") as f:
         faq_fallbacks = json.load(f)
 except FileNotFoundError:
     faq_fallbacks = {}
-    print("⚠️ Warning: faq.json not found. Only AI model will be used.")
+    print("⚠️ faq.json not found. AI will be used for all queries.")
 
-# Code Review Function
+# --------------------------
+# Code Review Endpoint Logic
+# --------------------------
 def analyze_code(code):
     if not code.strip():
         return "No code provided.", "", ""
     result = code_analyzer(code)
     return result[0]["label"], "Consider refactoring for better performance", "Medium"
 
-# Metadata Validator
+# --------------------------
+# Metadata Validation Logic
+# --------------------------
 def validate_metadata(metadata):
     if not metadata.strip():
         return "No metadata provided.", "", ""
+    # Example: Simulate unused field
     return "Field", "Unused field detected", "Remove it to improve performance"
 
-# Ask AI (Natural Language)
+# --------------------------
+# Ask AI with Fallback Logic
+# --------------------------
 def process_nlp_query(query):
     if not query.strip():
         return "No query provided."
 
     normalized = query.lower().strip()
-
-    # Check fallback JSON for exact match
     if normalized in faq_fallbacks:
         return faq_fallbacks[normalized]
 
-    # Fallback to AI model if not in faq.json
     prompt = f"""
-    You are a Salesforce and Apex expert. Provide a clear and accurate answer to the following question:\n\n{query}\n\nAnswer:
+    You are a Salesforce and Apex expert. Provide a clear, accurate answer to the following question:\n\n{query}\n\nAnswer:
     """
     result = nlp_model(
         prompt,
@@ -53,23 +57,24 @@ def process_nlp_query(query):
     )
 
     output = result[0]["generated_text"]
-
-    # Clean output
     if "Answer:" in output:
         output = output.split("Answer:")[-1]
 
+    # Remove duplicate lines
     lines = output.strip().splitlines()
     seen = set()
     unique_lines = [line.strip() for line in lines if line.strip() not in seen and not seen.add(line.strip())]
 
     return "\n".join(unique_lines).strip()
 
+# --------------------------
 # Gradio UI
+# --------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 AI Code Review & Metadata Validator")
+    gr.Markdown("# 🤖 Salesforce AI Code Review & Metadata Assistant")
 
     with gr.Tab("Code Review"):
-        code_input = gr.Textbox(label="Apex / LWC Code", lines=8)
+        code_input = gr.Textbox(label="Apex / LWC Code", lines=8, placeholder="Paste Apex or LWC code here")
         issue_type = gr.Textbox(label="Issue Type")
         suggestion = gr.Textbox(label="AI Suggestion")
         severity = gr.Textbox(label="Severity")
@@ -77,7 +82,7 @@ with gr.Blocks() as demo:
         code_button.click(analyze_code, inputs=code_input, outputs=[issue_type, suggestion, severity])
 
     with gr.Tab("Metadata Validation"):
-        metadata_input = gr.Textbox(label="Metadata XML", lines=8)
+        metadata_input = gr.Textbox(label="Metadata XML", lines=8, placeholder="Paste Salesforce metadata here")
         mtype = gr.Textbox(label="Type")
         issue = gr.Textbox(label="Issue")
         recommendation = gr.Textbox(label="Recommendation")